tvreg

tvreg: Variational Imaging Methods. The tvreg package performs total variation (TV) regularized image denoising, deconvolution, and inpainting. Three different noise models are supported: Gaussian (L2), Laplace (L1), and Poisson. The implementation solves the general TV restoration problem: min_u TV(u) + int lambda F(K*u,f) dx .to perform denoising, deconvolution, and inpainting as special cases. It is efficiently solved using the recent split Bregman method. Also included is an efficient implementation of Chan-Vese two-phase segmentation. All functions support grayscale, color, and arbitrary multichannel images.

Keywords for this software

Anything in here will be replaced on browsers that support the canvas element

References in zbMATH (referenced in 27 articles )

Showing results 1 to 20 of 27.
Sorted by year (citations)

1 2 next

  1. Archibald, Richard; Tran, Hoang: A dictionary learning algorithm for compression and reconstruction of streaming data in preset order (2022)
  2. Barbu, Tudor: Nonlinear PDE-based models for photon-limited image restoration (2021)
  3. Mukhoty, Bhaskar; Dutta, Subhajit; Kar, Purushottam: Robust non-parametric regression via incoherent subspace projections (2021)
  4. Pchelintsev, I. A.; Nasonov, A. V.; Krylov, A. S.: Regularization methods in the analysis of a series of scintillation fluorescence microscopy images (2021)
  5. Xu, Maoyuan; Xie, Xiaoping: An efficient feature-preserving image denoising algorithm based on a spatial-fractional anisotropic diffusion equation (2021)
  6. Holler, Martin; Weinmann, Andreas: Non-smooth variational regularization for processing manifold-valued data (2020)
  7. Kumar, Sumit; Jha, Rajib Kumar: An FPGA-based design for a real-time image denoising using approximated fractional integrator (2020)
  8. Mead, J.: ( \chi^2) test for total variation regularization parameter selection (2020)
  9. Ben Said, Ahmed; Hadjidj, Rachid; Foufou, Sebti: Total variation for image denoising based on a novel smart edge detector: an application to medical images (2019)
  10. Legarda-Saenz, Ricardo; Téllez Quiñones, Alejandro; Brito-Loeza, Carlos; Espinosa-Romero, Arturo: Variational phase recovering without phase unwrapping in phase-shifting interferometry (2019)
  11. Wang, Wei; Xia, Xiang-Gen; Zhang, Shengli; He, Chuanjiang; Chen, Ling: Vector total fractional-order variation and its applications for color image denoising and decomposition (2019)
  12. You, Juntao; Jiao, Yuling; Lu, Xiliang; Zeng, Tieyong: A nonconvex model with minimax concave penalty for image restoration (2019)
  13. Campagna, Rosanna; Crisci, Serena; Cuomo, Salvatore; Marcellino, Livia; Toraldo, Gerardo: Modification of TV-ROF denoising model based on split Bregman iterations (2017)
  14. Borkowski, Dariusz: Forward and backward filtering based on backward stochastic differential equations (2016)
  15. Lu, Wenqi; Duan, Jinming; Qiu, Zhaowen; Pan, Zhenkuan; Liu, Ryan Wen; Bai, Li: Implementation of high-order variational models made easy for image processing (2016)
  16. Maiseli, Baraka Jacob; Gao, Huijun: Robust edge detector based on anisotropic diffusion-driven process (2016)
  17. Orović, Irena; Lekić, Nedjeljko; Stanković, Srdjan: An analog-digital hardware for L-estimate space-varying image filtering (2016)
  18. Coll, Bartomeu; Duran, Joan; Sbert, Catalina: Half-linear regularization for nonconvex image restoration models (2015)
  19. Batard, Thomas; Bertalmío, Marcelo: On covariant derivatives and their applications to image regularization (2014)
  20. Burger, M.; Müller, J.; Papoutsellis, E.; Schönlieb, C. B.: Total variation regularization in measurement and image space for PET reconstruction (2014)

1 2 next